Towards forest condition assessment: evaluating small-footprint full-waveform airborne laser scanning data for deriving forest structural and compositional metrics

Sumnall, Matthew J.; Hill, Ross A.; Hinsley, Shelley A.. 2022 Towards forest condition assessment: evaluating small-footprint full-waveform airborne laser scanning data for deriving forest structural and compositional metrics [in special issue: Remote sensing applications for forest ecosystem monitoring and spatial modeling] Remote Sensing, 14 (20), 5081. 27, pp. https://doi.org/10.3390/rs14205081

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Abstract/Summary

Spatial data on forest structure, composition, regeneration and deadwood are required for informed assessment of forest condition and subsequent management decisions. Here, we estimate 27 forest metrics from small-footprint full-waveform airborne laser scanning (ALS) data using a random forest (RF) and automated variable selection (Boruta) approach. Modelling was conducted using leaf-off (April) and leaf-on (July) ALS data, both separately and combined. Field data from semi-natural deciduous and managed conifer plantation forests were used to generate the RF models. Based on NRMSE and NBias, overall model accuracies were good, with only two of the best 27 models having an NRMSE > 30% and/or NBias > 15% (Standing deadwood decay class and Number of sapling species). With the exception of the Simpson index of diversity for native trees, both NRMSE and NBias varied by less than ±4.5% points between leaf-on only, leaf-off only and combined leaf-on/leaf-off models per forest metric. However, whilst model performance was similar between ALS datasets, model composition was often very dissimilar in terms of input variables. RF models using leaf-on data showed a dominance of height variables, whilst leaf-off models had a dominance of width variables, reiterating that leaf-on and leaf-off ALS datasets capture different aspects of the forest and that structure and composition across the full vertical profile are highly inter-connected and therefore can be predicted equally well in different ways. A subset of 17 forest metrics was subsequently used to assess favourable conservation status (FCS), as a measure of forest condition. The most accurate RF models relevant to the 17 FCS indicator metrics were used to predict each forest metric across the field site and thresholds defining favourable conditions were applied. Binomial logistic regression was implemented to evaluate predicative accuracy probability relative to the thresholds, which varied from 0.73–0.98 area under the curve (AUC), where 11 of 17 metrics were >0.8. This enabled an index of forest condition (FCS) based on structure, composition, regeneration and deadwood to be mapped across the field site with reasonable certainty. The FCS map closely and consistently corresponded to forest types and stand boundaries, indicating that ALS data offer a feasible approach for forest condition mapping and monitoring to advance forest ecological understanding and improve conservation efforts.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3390/rs14205081
UKCEH and CEH Sections/Science Areas:	UKCEH Fellows
ISSN:	2072-4292
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	remote sensing, lidar, ALS, forest management, conservation, condition, random forest
NORA Subject Terms:	Ecology and Environment
Date made live:	21 Oct 2022 10:11 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533398

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